Learning both Weights and Connections for Efficient Neural Networks

Understand

Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems.

• Also, conventional networks fix the architecture before training starts; as a result, training cannot improve the architecture.
• To address these limitations, we describe a method to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important connections.
• Our method prunes redundant connections using a three-step method.